Online Behavior-Centric Adaptation for Bipedal Robot Sim-to-Real Transfer With Unmodeled Dynamics Mismatch

作者：Xuechao CHEN, Yidong Du, Zishun Zhou, Zhicheng Yuan, Qingrui Zhao, Fei Meng, Zhangguo Yu, Peng Lu, Qiang Huang · 发表于：IEEE Transactions on Automation Science and Engineering · 年份：2025 · DOI：10.1109/tase.2025.3648835 · 被引用次数：1 · 研究领域：Robotic Locomotion and Control、Reinforcement Learning in Robotics、Social Robot Interaction and HRI

Bipedal robots have achieved remarkable locomotion capabilities through reinforcement learning (RL), yet their real-world deployment remains hindered by the sim-to-real gap—dynamics mismatches between simulation and reality that degrade locomotion performance through behavioral deviations. This work introduces an online behavior adaptation framework that bridges this gap at the behavioral level by dynamically aligning emergent locomotion strategies with simulation-derived objectives. Our method integrates two core innovations: (1) a structured latent space constructed via an augmented Variational Autoencoder (VAE), which quantifies behavioral divergence through domain-invariant representations of locomotion patterns, and (2) a closed-loop adaptation module that maps latent-space deviations to real-time adjustments in low-level controller parameters. By reformulating sim-to-real transfer as a problem of behavioral alignment rather than explicit dynamics matching, the framework enables continuous adaptation to unmodeled dynamics mismatch without requiring system identification or offline retraining. Extensive experimental evaluations demonstrate the effectiveness of the proposed method, highlighting its potential to bridge the behavior gap between simulation and reality.